Food Security Training: Training on Plant Pest and Disease Organisms in Banana Crops in Tumbit Dayak Village
DOI:
https://doi.org/10.70609/i-com.v6i2.9531Keywords:
pisang, OPT, deteksi dini, pelatihan petani, ketahanan panganAbstract
Banana is a strategic commodity that plays an important role in food security and farmers’ livelihoods; however, its production is threatened by plant pests and diseases (PPD) such as Fusarium wilt and Sigatoka. Farmers’ limited knowledge in recognizing early disease symptoms is the main reason for implementing this community service activity in Tumbit Dayak Village. The implementation method consisted of three stages: preparation, core activities, and evaluation, using interactive lectures, group discussions, demonstrations, and field practice. The results showed an increase in participants’ knowledge, with the average pretest score rising from 55.5 to a post-test score of 80, along with improved observational skills and early detection in the field. Active participation also strengthened the relevance of the material to participants’ practical experience. This activity demonstrates that experience- and practice-based training effectively enhances farmers’ capacity in managing PPD. Therefore, contextual training is a crucial strategy for supporting sustainable banana production and local food security.
Keywords: banana, plant pests and diseases, early detection, farmer training, food security
References
Adiansyah, R., Lestiani, T., & Hairunnisa, M. (2026). I-Com : Indonesian Community Journal Dari Limbah Menjadi Nutrisi : Pelatihan Pembuatan Pupuk Organik Untuk. 6(1), 194–203. DOI: https://doi.org/10.70609/i-com.v6i1.9212
Ahmad, F., Martawi, N. M., Poerba, Y. S., Jong, H. d., Schouten, H. J., & Kema, G. H. J. (2020). Genetic Mapping of Fusarium Wilt Resistance in a Wild Banana Musa Acuminata Ssp. Malaccensis Accession. Theoretical and Applied Genetics, 133(12), 3409–3418. https://doi.org/10.1007/s00122-020-03677-y DOI: https://doi.org/10.1007/s00122-020-03677-y
Ainembabazi, J. H., Tripathi, L., Rusike, J., Abdoulaye, T., & Manyong, V. M. (2015). Ex-Ante Economic Impact Assessment of Genetically Modified Banana Resistant to Xanthomonas Wilt in the Great Lakes Region of Africa. Plos One, 10(9), e0138998. https://doi.org/10.1371/journal.pone.0138998 DOI: https://doi.org/10.1371/journal.pone.0138998
Bawin, Y., Zewdie, B., Ayalew, B., Roldán‐Ruiz, I., Janssens, S., Tack, A. J. M., Nemomissa, S., Tesfaye, K., Hylander, K., Honnay, O., & Ruttink, T. (2025). A Molecular Survey of the Occurrence of Coffee Berry Disease Resistant Coffee Cultivars Near the Wild Gene Pool of Arabica Coffee in Its Region of Origin in Southwest Ethiopia. Molecular Ecology Resources, 25(6). https://doi.org/10.1111/1755-0998.14085 DOI: https://doi.org/10.1111/1755-0998.14085
Bhargava, A., Shukla, A., Goswami, O. P., Alsharif, M. H., Uthansakul, P., & Uthansakul, M. (2024). Plant Leaf Disease Detection, Classification, and Diagnosis Using Computer Vision and Artificial Intelligence: A Review. Ieee Access, 12, 37443–37469. https://doi.org/10.1109/access.2024.3373001 DOI: https://doi.org/10.1109/ACCESS.2024.3373001
Chang, T., Salvucci, A. E., Crous, P. W., & Stergiopoulos, I. (2016). Comparative Genomics of the Sigatoka Disease Complex on Banana Suggests a Link Between Parallel Evolutionary Changes in Pseudocercospora Fijiensis and Pseudocercospora Eumusae and Increased Virulence on the Banana Host. Plos Genetics, 12(8), e1005904. https://doi.org/10.1371/journal.pgen.1005904 DOI: https://doi.org/10.1371/journal.pgen.1005904
Chou, M., Shrestha, S., Rioux, R., & Koch, P. (2021). Hyperlocal Variation in Soil Iron and the Rhizosphere Bacterial Community Determines Dollar Spot Development in Amenity Turfgrass. Applied and Environmental Microbiology, 87(10). https://doi.org/10.1128/aem.00149-21 DOI: https://doi.org/10.1128/AEM.00149-21
Dobbs, J., Kim, M., Dudley, N. S., Klopfenstein, N. B., Yeh, A., Hauff, R. D., Jones, T. C., Dumroese, R. K., Cannon, P., & Stewart, J. E. (2020). Whole Genome Analysis of the Koa Wilt Pathogen (Fusarium Oxysporum F. Sp. Koae) and the Development of Molecular Tools for Early Detection and Monitoring. BMC Genomics, 21(1). https://doi.org/10.1186/s12864-020-07156-y DOI: https://doi.org/10.1186/s12864-020-07156-y
Dong, J., Yin, H., Zheng, R., Yoo, S. J., & Gu, Y. H. (2023). PlantInfoCMS: Scalable Plant Disease Information Collection and Management System for Training AI Models. Sensors, 23(11), 5032. https://doi.org/10.3390/s23115032 DOI: https://doi.org/10.3390/s23115032
Elaraby, A., Hamdy, W., & Alruwaili, M. (2022). Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer. Computers Materials & Continua, 71(2), 4019–4031. https://doi.org/10.32604/cmc.2022.022161 DOI: https://doi.org/10.32604/cmc.2022.022161
Fofana, O., Bi, R. D. R. V., & Kouassi, K. C. (2025). Evaluation of Biocontrol of Mycosphaerella Fijiensis by Five Species of Rhizosphere Fungi of Plantain. Zeitschrift Für Allgemeine Mikrobiologie, 65(9). https://doi.org/10.1002/jobm.70020 DOI: https://doi.org/10.1002/jobm.70020
Frimpong, B. N., Oppong, A., Prempeh, R., Appiah-Kubi, Z., Abrokwah, L. A., Mochiah, M. B., Lamptey, J. N. L., Manu-Aduening, J., & Pita, J. S. (2021). Farmers’ Knowledge, Attitudes and Practices Towards Management of Cassava Pests and Diseases in Forest Transition and Guinea Savannah Agro-Ecological Zones of Ghana. Gates Open Research, 4, 101. https://doi.org/10.12688/gatesopenres.13114.2 DOI: https://doi.org/10.12688/gatesopenres.13114.2
Gaidashova, S., Kajuga, J., Blomme, G., Ocimati, W., Kearsley, E., Hufkens, K., Nishimwe, I., Ndayizeye, J. P., & Omondi, B. A. (2025). Banana Bunchy Top Disease in Rwanda: Identifying Disease Presence and Potential Areas at Risk. Phytopathology, 115(12), 1728–1736. https://doi.org/10.1094/phyto-04-25-0135-r DOI: https://doi.org/10.1094/PHYTO-04-25-0135-R
Görlich, F., Marks, E., Mahlein, A., König, K., Lottes, P., & Stachniss, C. (2021). UAV-Based Classification of Cercospora Leaf Spot Using RGB Images. Drones, 5(2), 34. https://doi.org/10.3390/drones5020034 DOI: https://doi.org/10.3390/drones5020034
Hassan, S. M., Amitab, K., Jasiński, M., Leonowicz, Z., Jasińska, E., Novák, T., & Maji, A. K. (2022). A Survey on Different Plant Diseases Detection Using Machine Learning Techniques. Electronics, 11(17), 2641. https://doi.org/10.3390/electronics11172641 DOI: https://doi.org/10.3390/electronics11172641
Lamba, M., Gigras, Y., & Dhull, A. (2021). Classification of Plant Diseases Using Machine and Deep Learning. Open Computer Science, 11(1), 491–508. https://doi.org/10.1515/comp-2020-0122 DOI: https://doi.org/10.1515/comp-2020-0122
Lee, E. M. (2019). Highly geographical specificity of metabolomic traits among Korean domestic soybeans (Glycine max). Food Research International, 120, 12–18. https://doi.org/10.1016/j.foodres.2019.02.021 DOI: https://doi.org/10.1016/j.foodres.2019.02.021
Liebig, T., Jassogne, L., Rahn, E., Läderach, P., Poehling, H., Kucel, P., Asten, P. v., & Avelino, J. (2016). Towards a Collaborative Research: A Case Study on Linking Science to Farmers’ Perceptions and Knowledge on Arabica Coffee Pests and Diseases and Its Management. Plos One, 11(8), e0159392. https://doi.org/10.1371/journal.pone.0159392 DOI: https://doi.org/10.1371/journal.pone.0159392
Mikaberidze, A., Cruz, C. D., Zerihun, A., Barreto, A., Beck, P. S. A., Calderón, R., Camino, C., Campbell, R. E., Delalieux, S., Fabre, F., Falla, E. K., Fraser, S., Gold, K. M., Góngora‐Canul, C., Hamelin, F., Harteveld, D. O. C., Hong, C., Leclerc, M., Lee, D.-Y., … Cunniffe, N. J. (2025). Opportunities and Challenges in Combining Optical Sensing and Epidemiological Modeling. Phytopathology, 115(10), 1260–1285. https://doi.org/10.1094/phyto-11-24-0359-fi DOI: https://doi.org/10.1094/PHYTO-11-24-0359-FI
Murugesan, S., Chinnadurai, J., Srinivasan, S., Mathivanan, S. K., Chandan, R. R., & Moorthy, U. (2025). Robust Multiclass Classification of Crop Leaf Diseases Using Hybrid Deep Learning and Grad-Cam Interpretability. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-14847-7 DOI: https://doi.org/10.1038/s41598-025-14847-7
Nyine, M., Wang, S., Kiani, K., Jordan, K. W., Liu, S., Byrne, P. F., Haley, S. D., Baenziger, P. S., Chao, S., Bowden, R. L., & Akhunov, E. (2019). Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits. G3 Genes|genome|genetics, 9(1), 125–133. https://doi.org/10.1534/g3.118.200664 DOI: https://doi.org/10.1534/g3.118.200664
Rehman, A., Abunadi, I., Alamri, F. S., Ali, H., Bahaj, S. A., & Saba, T. (2024). An Intelligent Deep Augmented Model for Detection of Banana Leaves Diseases. Microscopy Research and Technique, 88(1), 53–64. https://doi.org/10.1002/jemt.24681 DOI: https://doi.org/10.1002/jemt.24681
Ritter, T., Álvarez, D., Mosquera, L. D. S., Martey, E., & Mockshell, J. (2024). A Socioeconomic and Cost Benefit Analysis of Tropical Race 4 (TR4) Prevention Methods Among Banana Producers in Colombia. Plos One, 19(10), e0311243. https://doi.org/10.1371/journal.pone.0311243 DOI: https://doi.org/10.1371/journal.pone.0311243
Salgado, E., Guevara, F., Muentes, C., Flores, F., & Magdama, F. (2025). Identifying Bacterial and Fungal Communities Associated With Fusarium-Wilt Symptomatic and Non-Symptomatic ‘Gros Michel’ Banana Plants in Ecuador. Frontiers in Cellular and Infection Microbiology, 15. https://doi.org/10.3389/fcimb.2025.1572860 DOI: https://doi.org/10.3389/fcimb.2025.1572860
Shafik, W., Tufail, A., Silva, L. C. D., & Apong, R. A. A. H. M. (2024). Using Transfer Learning-Based Plant Disease Classification and Detection for Sustainable Agriculture. BMC Plant Biology, 24(1). https://doi.org/10.1186/s12870-024-04825-y DOI: https://doi.org/10.1186/s12870-024-04825-y
Shafik, W., Tufail, A., Silva, L. C. D., & Apong, R. A. A. H. M. (2025). A Lightweight Deep Learning Model for Multi-Plant Biotic Stress Classification and Detection for Sustainable Agriculture. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-90487-1 DOI: https://doi.org/10.1038/s41598-025-90487-1
Shinde, N., & Ambhaikar, A. (2024). Comprehensive Survey on Datasets, Models, and Future Directions in Plant Disease Prediction. International Journal of Image and Graphics, 26(03). https://doi.org/10.1142/s0219467826500221 DOI: https://doi.org/10.1142/S0219467826500221
Shultana, R., Zuan, A. T. K., Naher, U. A., Islam, A. K. M. M., Rana, M. M., Rashid, M. H., Irin, I. J., Islam, S. S., Rim, A. A., & Hasan, A. K. (2022). The PGPR Mechanisms of Salt Stress Adaptation and Plant Growth Promotion. Agronomy, 12(10). https://doi.org/10.3390/agronomy12102266 DOI: https://doi.org/10.3390/agronomy12102266
Toda, Y., & Okura, F. (2019). How Convolutional Neural Networks Diagnose Plant Disease. Plant Phenomics, 2019, 9237136. https://doi.org/10.34133/2019/9237136 DOI: https://doi.org/10.34133/2019/9237136
Zhong, Y., Chen, Z., & Cheng, Z. (2022). Different Scales of Gene Duplications Occurring at Different Times Have Jointly Shaped the NBS-LRR Genes in Prunus Species. Molecular Genetics and Genomics, 297(1), 263–276. https://doi.org/10.1007/s00438-021-01849-z DOI: https://doi.org/10.1007/s00438-021-01849-z
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